Better Estimated IEM Input Parameters Using Random Fractal Geometry Applied on Multi-Frequency SAR Data

Ali Ghafouri,Mojtaba Dehmollaian,Jalal Amini,Mohammad Kavoosi

doi:10.3390/rs9050445

Ali Ghafouri, Mojtaba Dehmollaian + Show 2 more

Open Access

https://doi.org/10.3390/rs9050445

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Abstract

Microwave remote sensing can measure surface geometry. Via the processing of the Synthetic Aperture Radar (SAR) data, the earth surface geometric parameters can be provided for geoscientific studies, especially in geological mapping. For this purpose, it is necessary to model the surface roughness against microwave signal backscattering. Of the available models, the Integral Equation Model (IEM) for co-polarized data has been the most frequently used model. Therefore, by the processing of the SAR data using this model, the surface geometry can be studied. In the IEM, the surface roughness geometry is calculable via the height statistical parameter, the rms-height. However, this parameter is not capable enough to represent surface morphology, since it only measures the surface roughness in the vertical direction, while the roughness dispersion on the surface is not included. In this paper, using the random fractal geometry capability, via the implementation of the power-law roughness spectrum, the precision and correctness of the surface roughness estimation has been improved by up to 10%. Therefore, the random fractal geometry is implemented through the calculation of the input geometric parameters of the IEM using the power-law surface spectrum and the spectral slope. In this paper, the in situ roughness measurement data, as well as SAR images at frequencies of L, C, and X, have been used to implement and evaluate the proposed method. Surface roughness, according to the operational frequencies, exhibits a fractal or a diffractal behavior.

Highlights

Studying the surface roughness geometry, especially studying the natural surfaces, requires appropriate satellite data and processing methodologies [1,2,3]
The implementation of the fractal geometry in this andthe one power-law implementation of the fractal geometry in this paper is paper is performed through the spectral slope parameter (α), which shows the coincidence of the performed through the spectral slope parameter (α), which shows the coincidence of the power-law power-law with the earth surface
Using the random fractal geometry via the implementation of the power-law model in the backscattering modeling offers results which are up to 10% better for the calculation of the surface geometry

Summary

Introduction

Studying the surface roughness geometry, especially studying the natural surfaces, requires appropriate satellite data and processing methodologies [1,2,3]. Data acquired by the airborne and space-borne sensors has made it possible to examine the surface roughness, which provides useful information for geoscientists and geologists. The backscattered signal in all polarizations is affected by surface roughness, and contains the surface geometry information [4,5,6]. To measure the surface geometry using the SAR data, the surface geometric parameter(s) must be modeled against the backscattering coefficient on each polarization. To describe and differentiate the patterns and the geometric surface texture, it is indispensable to model the interaction between the backscattered signals and the surface properties.

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Discussion

Conclusion